Eye center localization method and localization system thereof

ABSTRACT

An eye center localization method includes performing an image sketching step, a frontal face generating step, an eye center marking step and a geometric transforming step. The image sketching step is performed to drive a processing unit to sketch a face image from the image. The frontal face generating step is performed to drive the processing unit to transform the face image into a frontal face image according to a frontal face generating model. The eye center marking step is performed to drive the processing unit to mark a frontal eye center position information on the frontal face image. The geometric transforming step is performed to drive the processing unit to calculate two rotating variables between the face image and the frontal face image, and calculate the eye center position information according to the two rotating variables and the frontal eye center position information.

RELATED APPLICATIONS

This application claims priority to Taiwan Application Serial Number110118349, filed May 20, 2021, which is herein incorporated byreference.

BACKGROUND Technical Field

The present disclosure relates to a localization method and alocalization system. More particularly, the present disclosure relatesto an eye center localization method and a localization system thereof.

Description of Related Art

An eye center localization method can calculate an eye center coordinatefrom an image with human face. However, the conventional eye centerlocalization methods are applied to an image of frontal face or image ofhead posture in specific rotating angle. If the rotating angle of theimage is too big, the conventional eye center localization method cannotlocate the eye center from the image correctly.

Thus, a method and a system for locating the eye center which is notrestricting by the rotating angle of the head in the image arecommercially desirable.

SUMMARY

According to one aspect of the present disclosure, an eye centerlocalization method is configured to locate an eye center positioninformation from an image, the eye center localization method includesperforming an image sketching step, a frontal face generating step, aneye center marking step and a geometric transforming step. The imagesketching step is performed to drive a processing unit to sketch a faceimage from the image of a database. The frontal face generating step isperformed to drive the processing unit to transform the face image intoa frontal face image according to a frontal face generating model. Theeye center marking step is performed to drive the processing unit tomark a frontal eye center position information on the frontal face imageaccording to a gradient method. The geometric transforming step isperformed to drive the processing unit to calculate two rotatingvariables between the face image and the frontal face image, andcalculate the eye center position information according to the tworotating variables and the frontal eye center position information.

According to another aspect of the present disclosure, an eye centerlocalization system is configured to locate an eye center positioninformation from an image, the eye center localization system includes adatabase and a processing unit. The database is configured to access theimage, a frontal face generating model and a gradient method. Theprocessing unit is electrically connected to the database, theprocessing unit receives the image, the frontal face generating modeland the gradient method and is configured to implement an eye centerlocalization method includes performing an image sketching step, afrontal face generating step, an eye center marking step and a geometrictransforming step. The image sketching step is performed to sketch aface image from the image. The frontal face generating step is performedto transform the face image into a frontal face image according to thefrontal face generating model. The eye center marking step is performedto mark a frontal eye center position information on the frontal faceimage according to the gradient method. The geometric transforming stepis performed to calculate two rotating variables between the face imageand the frontal face image, and calculate the eye center positioninformation according to the two rotating variables and the frontal eyecenter position information.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be more fully understood by reading thefollowing detailed description of the embodiment, with reference made tothe accompanying drawings as follows:

FIG. 1 shows a flow chart of an eye center localization method accordingto a first embodiment of the present disclosure.

FIG. 2 shows a flow chart of an eye center localization method accordingto a second embodiment of the present disclosure.

FIG. 3 shows a schematic view of an image of an image sketching step ofthe eye center localization method of FIG. 2 .

FIG. 4 shows a schematic view of a face image of the image sketchingstep of the eye center localization method of FIG. 2 .

FIG. 5 shows a schematic view of a frontal face image of a frontal facegenerating step of the eye center localization method of FIG. 2 .

FIG. 6 shows a schematic view of a rotating variable of a geometrictransforming step of the eye center localization method of FIG. 2 .

FIG. 7 shows a schematic view of another rotating variable of thegeometric transforming step of the eye center localization method ofFIG. 2 .

FIG. 8 shows a schematic view of a model training step of the eye centerlocalization method of FIG. 2 .

FIG. 9 shows a block diagram of an eye center localization systemaccording to a third embodiment of the present disclosure.

DETAILED DESCRIPTION

The embodiment will be described with the drawings. For clarity, somepractical details will be described below. However, it should be notedthat the present disclosure should not be limited by the practicaldetails, that is, in some embodiment, the practical details isunnecessary. In addition, for simplifying the drawings, someconventional structures and elements will be simply illustrated, andrepeated elements may be represented by the same labels.

It will be understood that when an element (or device) is referred to asbe “connected to” another element, it can be directly connected to otherelement, or it can be indirectly connected to the other element, thatis, intervening elements may be present. In contrast, when an element isreferred to as be “directly connected to” another element, there are nointervening elements present. In addition, the terms first, second,third, etc. are used herein to describe various elements or components,these elements or components should not be limited by these terms.Consequently, a first element or component discussed below could betermed a second element or component.

Please refer to FIG. 1 . FIG. 1 shows a flow chart of an eye centerlocalization method 100 according to a first embodiment of the presentdisclosure. The eye center localization method 100 is configured tolocate an eye center position information from an image. The eye centerlocalization method 100 includes performing an image sketching step S01,a frontal face generating step S02, an eye center marking step S03 and ageometric transforming step S04. The image sketching step S01 isperformed to drive a processing unit to sketch a face image from theimage of a database. The frontal face generating step S02 is performedto drive the processing unit to transform the face image into a frontalface image according to a frontal face generating model. The eye centermarking step S03 is performed to drive the processing unit to mark afrontal eye center position information on the frontal face imageaccording to a gradient method. The geometric transforming step S04 isperformed to drive the processing unit to calculate two rotatingvariables between the face image and the frontal face image, andcalculate the eye center position information according to the tworotating variables and the frontal eye center position information. Indetail, the image can be a normal view face image, a non-normal viewface image, a face image with shaded eye region or a face image withdefect region. Thus, the eye center localization method 100 of thepresent disclosure locates the eye center position information from anon-normal view face image.

Please refer to FIG. 2 to FIG. 7 . FIG. 2 shows a flow chart of an eyecenter localization method 100 a according to a second embodiment of thepresent disclosure. FIG. 3 shows a schematic view of an image I of animage sketching step S11 of the eye center localization method 100 a ofFIG. 2 . FIG. 4 shows a schematic view of a face image I_(f) of theimage sketching step S11 of the eye center localization method 100 a ofFIG. 2 . FIG. 5 shows a schematic view of a frontal face image IF_(f) ofa frontal face generating step S12 of the eye center localization method100 a of FIG. 2 . FIG. 6 shows a schematic view of a rotating variableface_(θ1) of a geometric transforming step S14 of the eye centerlocalization method 100 a of FIG. 2 . FIG. 7 shows a schematic view ofanother rotating variable face_(θ2) of the geometric transforming stepS14 of the eye center localization method 100 a of FIG. 2 . The eyecenter localization method 100 a includes performing an image sketchingstep S11, a frontal face generating step S12, an eye center marking stepS13 and a geometric transforming step S14. The image sketching step S11is performed to drive a processing unit to sketch the face image I_(f)from the image I of a database. The image sketching step S11 includes afacial feature marking step S112 and a facial area sketching step S114.The facial feature marking step S112 is performed to mark a chin featurepoint p₈, right eye feature points p₃₆, p₃₇, p₃₈, p₃₉, p₄₀, p₄₁ and lefteye feature points p₄₂, p₄₃, p₄₄, p₄₅, p₄₆, p₄₇ on the image I. Thefacial area sketching step S114 is performed to sketch the face imageI_(f) according to the chin feature point p₈, the right eye featurepoints p₃₆-p₄₁ and the left eye feature points p₄₂-p₄₇.

Please refer to FIG. 3 and FIG. 4 . The facial feature marking step S112is performed to mark the chin feature point p₈, fetch a plurality ofright eye feature points p₃₆-p₄₁ and a plurality of left eye featurepoints p₄₂-p₄₇ around the right eye and the left eye, respectively, andpredict an estimate right eye center coordinate (AEC_(r)_x, AEC_(r)_y)and an estimate left eye center coordinate (AEC_(l)_x, AEC_(l)_y)according to the right eye feature points p₃₆-p₄₁ and the left eyefeature points P₄₂-P_(47.) The calculating method of the estimate righteye center coordinate (AEC_(r)_x, AEC_(r)_y) is satisfied by a formula(1). The facial area sketching step S114 is performed to calculate aface height face_(h) and a face width face_(w) of a facial area of theimage I according to the estimate right eye center coordinate(AEC_(r)_x, AEC_(r)_y) and the estimate left eye center coordinate(AEC_(I)_x, AEC_(I)_y), and calculate a range of the facial area tosketch the face image I_(f). The calculating method of the face heightface_(h) and the face width face_(w) are satisfied by a formula (2) to aformula (5).

$\begin{matrix}{\left( {{{AEC}_{r}{\_ x}},{{AEC}_{r}{\_ y}}} \right) = {\frac{\left( {p_{36} - p_{39}} \right) + \left( {p_{37} - p_{40}} \right) + \left( {p_{38} - p_{41}} \right)}{2}.}} & (1)\end{matrix}$ $\begin{matrix}{D^{\star} = {\begin{matrix}{\arg\max} \\D\end{matrix}\left\{ {\begin{matrix}{D_{r} = {{{AEC}_{r} - p_{8}}}_{2}} \\{D_{I} = {{{AEC}_{I} - p_{8}}}_{2}}\end{matrix}.} \right.}} & (2)\end{matrix}$ $\begin{matrix}{{face}_{h} = {{face}_{w} = {D^{*} + {\left( \frac{D^{*}}{\alpha_{1}} \right).}}}} & (3)\end{matrix}$ $\begin{matrix}{\left( {{ULC\_ x},{ULC\_ y}} \right) = {\left\{ {{{{AEC}_{r}{\_ x}} - \frac{D^{*}}{\alpha_{2}}},{{{AEC}_{r}{\_ y}} - \frac{D^{*}}{\alpha_{2}}}} \right\}.}} & (4)\end{matrix}$ $\begin{matrix}{I_{f} = {\sum_{x = {ULC\_ x}}^{{ULC\_ x} + {face}_{w}}{\sum_{y = {ULC\_ y}}^{{ULC\_ y} + {face}_{h}}{{I\left( {x,y} \right)}.}}}} & (5)\end{matrix}$D* is a maximum value of an Euclidean distance from the estimate righteye center coordinate (AEC_(r)_x, AEC_(r)_y) and the estimate left eyecenter coordinate (AEC_(I)_x, AEC_(l)_y) to the chin feature point p₈.α₁ and α₂ are adjustable coefficients. (ULC_x, ULC_y) is a coordinate ofa begin point of sketching the facial area.

Please refer to FIG. 5 . The frontal face generating step S12 isperformed to drive the processing unit to transform the face image I_(f)into the frontal face image IF_(f) according to a frontal facegenerating model. In detail, the frontal face generating model istrained from a complete representation-generative adversarial network(CR-GAN) and a supervised-learning. The CR-GAN and thesupervised-learning are conventional and will not be described again.

The eye center marking step S13 is performed to drive the processingunit to mark a frontal eye center position information C on the frontalface image IF_(f) according to a gradient method. The eye center markingstep S13 includes a weight adjusting step S132. The weight adjustingstep S132 is performed to adjust a weight value of the frontal faceimage IF_(f) according to an Iris-Ripple filter method. Moreparticularly, the frontal eye center position information C includes afrontal right eye center coordinate (C_(r)_x, C_(r)_y) and a frontalleft eye center coordinate (C_(l)_x, C_(l)_y). During marking thefrontal eye center position information C, the shadow of the specificarea (such as an eyelid area, a canthus area and an eyebrow area) of thefrontal face image IF_(f) will interfere the gradient of the frontalface image IF_(f), and reduce the accuracy of marking the frontal eyecenter position information C by the gradient method. Thus, adjustingthe weight value by the Iris-Ripple method can increase the locatingaccuracy. The Iris-Ripple filter method is satisfied by a formula (6)and a formula (7), and the Iris-Ripple method combines with the gradientmethod is satisfied by a formula (8).

$\begin{matrix}{R_{r}^{*} = {\begin{matrix}{\arg\max} \\R_{r}\end{matrix}\left\{ {\begin{matrix}{{p_{36} - {AEC}_{r}}}_{2} \\{{p_{39} - {AEC}_{r}}}_{2}\end{matrix}.} \right.}} & (6)\end{matrix}$ $\begin{matrix}{{{IR}\left( {x,y} \right)} = {\sum_{r = 0}^{\frac{{Eye}_{m}}{2}}\left\{ {\begin{matrix}{{{r\tau\left\{ {{Lx},{Ly}} \right\}} = {\omega\left( {1 - \frac{r}{R_{r}^{\star}}} \right)}},{{{if}r} \leq R_{r}^{\star}}} \\{{{r\tau\left\{ {{Lx},{Ly}} \right\}} = {\omega(0)}},{{{if}r} > R_{r}^{\star}}}\end{matrix}.} \right.}} & (7)\end{matrix}$ $\begin{matrix}{C = {\begin{matrix}{\arg\max} \\C^{\prime}\end{matrix}{\left\{ {\frac{1}{N}{\sum_{x = 1}^{{Eye}_{m}}{\sum_{y = 1}^{{Eye}_{n}}{{{IR}\left( {x,y} \right)} \cdot \text{ }\left\lbrack {\alpha_{3} - {{{IF}_{e}\left( {{AEC}\left( {x,y} \right)} \right)} \cdot \left( {{d^{t}\left( {x,y} \right)} \cdot {g\left( {x,y} \right)}} \right)^{2}}} \right\rbrack}}}} \right\}.}}} & (8)\end{matrix}$R_(r)* represents the eye area, IR(x, y) represents the coordinate ofthe current adjusting pixel, Eye_(m) represents a column number of thepixel of the eye area, Eye_(n) represents a row number of the pixel ofthe eye area, r represents a radius of the eye area, τ=2π, {Lx, Ly} is acoordinate of a pixel which is calculated by a radius perimeter takingthe estimate right eye center coordinate (AEC_(r)_x, AEC-_(r)_y) and theestimate left eye center coordinate (AEC_(l)_x, AEC_(l)_y) as centers,ω(⋅) is a weight value before calculating, C′ represents a current eyecenter coordinate, N is a pixel number of the eye area, IF_(e)(AEC(x,y)) is a strength of predicting the center of the eye area, d(x, y) is adisplacement vector between c and p(x, y), g(x, y) is a gradient vector,and α₃ is a maximum grayscale.

Please refer to FIG. 6 and FIG. 7 , the geometric transforming step S14is performed to drive the processing unit to calculate two rotatingvariables face_(θ1), face_(θ2) between the face image I_(f) and thefrontal face image IF_(f), and calculate the eye center positioninformation I_(ec) according to the two rotating variables face_(θ1),face_(θ2) and the frontal eye center position information C. Thegeometric transforming step S14 includes a rotating variable calculatingstep S142 and an eye center transforming step S144. The rotatingvariable calculating step S142 is performed to calculate the tworotating variables face_(θ1), face_(θ2) between the face image I_(f) andthe frontal face image IF_(f) according to a linear relation equation,the linear relation equation is satisfied with a formula (9).

$\begin{matrix}\left\{ {\begin{matrix}{{face}_{\theta 1} = {{\tan^{- 1}\left( {❘\frac{{m_{1}\left( L_{1} \right)} - {m_{2}\left( L_{2} \right)}}{1 + {{m_{1}\left( L_{1} \right)} \star {m_{2}\left( L_{2} \right)}}}❘} \right)} \star \frac{180}{\pi}}} \\{{face}_{\theta 2} = {{\tan^{- 1}\left( {❘\frac{{m_{1}\left( L_{1} \right)} - {m_{3}\left( L_{3} \right)}}{1 + {{m_{1}\left( L_{1} \right)} \star {m_{3}\left( L_{3} \right)}}}❘} \right)} \star \frac{180}{\pi}}}\end{matrix}.} \right. & (9)\end{matrix}$The rotating variable face_(θ1) is a rotating variable between the faceimage I_(f) and the frontal face image IF_(f) which is rotating alongthe x axis (i.e., yaw rot.), the rotating variable face_(θ2) is arotating variable between the face image I_(f) and the face transformingimage I_(f)′ which is rotating along the z axis (i.e., roll rot.). L1 isa linear relation equation between the estimate right eye centercoordinate (AEC_(r)_x, AEC_(r)_y) and the estimate left eye centercoordinate (AEC_(l)_x, AEC_(l)_y), L2 is a linear relation equationbetween the frontal right eye center coordinate (C_(r)_x, C_(r)_y) andthe frontal left eye center coordinate (C_(l)_x, C_(l)_y), and L3 is alinear relation equation between the estimate right eye centercoordinate (AEC_(r)_x, AEC_(r)_y) and the estimate left eye centercoordinate (AEC_(l)_x, AEC_(l)_y) after transforming into thethree-dimensional coordinate. m1 is a slope of the linear relationequation L1, m2 is a slope of the linear relation equation L2, and m3 isa slope of the linear relation equation L3.

The eye center transforming step S144 is performed to predict a depthtransforming coordinate (I_(erc1)_x, I_(erc1)_y) of the face image I_(f)with respect to the frontal face image IF_(f) according to the tworotating variables face_(θ1), face_(θ2), and calculate the eye centerposition information I_(eC) according to the depth transformingcoordinate (I_(erc1)_x, I_(erc1)_y). The eye center transforming stepS144 predicts the depth transforming coordinate (I_(erc1)_x, I_(erc1)_y)by a formula (10):

$\begin{matrix}{\left( {{I_{{erC}1}{\_ x}},{I_{{erC}1}{\_ y}}} \right) = {\left\{ {\frac{{C_{r}{\_ x}} - {{IF}_{{AEC}_{r}}{\_ x}}}{{\cos\left( {face}_{\theta 1} \right)} \star {\cos\left( {face}_{\theta 2} \right)}},\frac{{C_{r}{\_ y}} - {{IF}_{{AEC}_{r}}{\_ y}}}{{\cos\left( {face}_{\theta 1} \right)} \star {\cos\left( {face}_{\theta 2} \right)}}} \right\}.}} & (10)\end{matrix}$The eye center position information I_(eC) includes a right eye centercoordinate (I_(erC)_x, I_(erC)_y) and a left eye center coordinate(I_(elC)_x, I_(elC)_y), and (IF_(AECr)_x, IF_(AECr)_y) is a frontal faceestimate right eye center coordinate.

In detail, after the formula (10) obtains the depth transformingcoordinate (I_(erc1)_x, I_(erc1)_y), in order to avoid the differencebetween the frontal eye center position information C calculated by thefrontal face image IF_(f) which is generated from the frontal facegenerating model and the actual value. The eye center transforming step144 can adjust the depth transforming coordinate (I_(erc1)_x,I_(erc1)_y) by a formula (11):

$\begin{matrix}{\left( {{I_{erC}{\_ x}},{I_{erC}{\_ y}}} \right) = {\left\{ {\left\lbrack {\left( {{I_{{erC}1}{\_ x}} + {{\alpha 4}\left( \frac{{I_{{erC}2}{\_ x}} - {{AEC}_{r}{\_ x}}}{{\cos\left( {face}_{\theta 1} \right)} \star {\cos\left( {face}_{\theta 2} \right)}} \right)}} \right) \star \text{ }{\cos\left( {face}_{\theta 1} \right)} \star {\cos\left( {face}_{\theta 2} \right)}} \right\rbrack,\text{ }\left\lbrack {\left( {{I_{{erC}1}{\_ y}} + {{AEC}_{r}{\_ y}}} \right) \star {\cos\left( {face}_{\theta 1} \right)} \star {\cos\left( {face}_{\theta 2} \right)}} \right\rbrack} \right\}.}} & (11)\end{matrix}$(I_(erc2)_x, I_(erc2)_y) is a frontal right eye center coordinate whichhas a big difference with the actual value, α₄ is a correctioncoefficient. Thus, the eye center localization method 100 a of thepresent disclosure adjusts the eye center position information I_(eC) bythe correction coefficient α₄ to avoid the difference caused by thefrontal face image IF_(f), thereby increasing the accuracy of the eyecenter position information I_(eC).

Please refer to FIG. 2 to FIG. 8 . FIG. 8 shows a schematic view of amodel training step S15 of the eye center localization method 100 a ofFIG. 2 . The eye center localization method 100 a of FIG. 2 can furtherinclude a model training step S15. The model training step S15 isperformed to drive the processing unit to train the face image I_(f),the eye center position information I_(eC), the frontal face imageIF_(f) and the frontal eye center position information C to generate aneye center locating model 40. In other words, the model training stepS15 sketches the eye region images I_(f)_r, IF_(f)_r from the face imageIf and the frontal face image IF_(f), respectively, takes the eye regionbefore marking the eye center position information I_(eC) and thefrontal eye center position information C as a first training sample Tx,and takes the eye region after marking the eye center positioninformation I_(eC) and the frontal eye center position information C asa second training sample Ty. The first training sample Tx and the secondtraining sample Ty are trained by an image translation learning togenerate the eye center locating model 40. Thus, the eye centerlocalization method 100 a of the present disclosure can predict the eyecenter position information I_(eC) from the image I directly by the eyecenter locating model 40.

Please refer to FIG. 9 . FIG. 9 shows a block diagram of an eye centerlocalization system 200 according to a third embodiment of the presentdisclosure. The eye center localization system 200 is configured tolocate an eye center position information I_(ec) from an image I. Theeye center localization system 200 includes a database 210 and aprocessing unit 220.

The database 210 is configured to access the image I, a frontal facegenerating model 20 and a gradient method 30. In detail, the database210 can be a memory or other data accessing element.

The processing unit 220 is electrically connected to the database 210,the processing unit 220 receives the image I, the frontal facegenerating model 20 and the gradient method 30, and the processing unit220 is configured to implement the eye center localization methods 100,100 a. In detail, the processing unit 220 can be a microprocessor, acentral processing unit (CPU) or other electronic processing unit, butthe present disclosure is not limited thereto. Thus, the eye centerlocalization system 200 locates the eye center position informationI_(eC) from an image I with non-frontal face.

According to the aforementioned embodiments and examples, the advantagesof the present disclosure are described as follows.

1. The eye center localization method and localization system thereoflocate the eye center position information from image with non-frontalface.

2. The eye center localization method of the present disclosure adjuststhe eye center position information by the correction coefficient toavoid the difference caused by the frontal face image, therebyincreasing the accuracy of the eye center position information.

3. The eye center localization method of the present disclosure canpredict the eye center position information from the image directly bythe eye center locating model.

Although the present disclosure has been described in considerabledetail with reference to certain embodiments thereof, other embodimentsare possible. Therefore, the spirit and scope of the appended claimsshould not be limited to the description of the embodiments containedherein.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of the presentdisclosure without departing from the scope or spirit of the disclosure.In view of the foregoing, it is intended that the present disclosurecover modifications and variations of this disclosure provided they fallwithin the scope of the following claims.

What is claimed is:
 1. An eye center localization method, which isconfigured to locate an eye center position information from an image,the eye center localization method comprising: performing an imagesketching step to drive a processing unit to sketch a face image fromthe image of a database; performing a frontal face generating step todrive the processing unit to transform the face image into a frontalface image according to a frontal face generating model; performing aneye center marking step to drive the processing unit to mark a frontaleye center position information on the frontal face image according to agradient method; and performing a geometric transforming step to drivethe processing unit to calculate two rotating variables between the faceimage and the frontal face image, and calculate the eye center positioninformation according to the two rotating variables and the frontal eyecenter position information; wherein the eye center marking stepcomprises: performing a weight adjusting step to adjust a weight valueof the frontal face image according to an Iris-Ripple filter method. 2.The eye center localization method of claim 1, wherein the imagesketching step comprises: performing a facial feature marking step tomark a chin feature point, a right eye feature point and a left eyefeature point on the image; and performing a facial area sketching stepto sketch the face image according to the chin feature point, the righteye feature point and the left eye feature point.
 3. The eye centerlocalization method of claim 1, wherein the geometric transforming stepcomprises: performing a rotating variable calculating step to calculatethe two rotating variables between the face image and the frontal faceimage according to a linear relation equation; and performing an eyecenter transforming step to predict a depth transforming coordinate ofthe face image with respect to the frontal face image according to thetwo rotating variables, and calculate the eye center positioninformation according to the depth transforming coordinate.
 4. The eyecenter localization method of claim 1, further comprising: performing amodel training step to drive the processing unit to train the faceimage, the eye center position information, the frontal face image andthe frontal eye center position information to generate an eye centerlocating model.
 5. An eye center localization system, which isconfigured to locate an eye center position information from an image,the eye center localization system comprising: a database configured toaccess the image, a frontal face generating model and a gradient method;and a processing unit electrically connected to the database, whereinthe processing unit receives the image, the frontal face generatingmodel and the gradient method and is configured to implement an eyecenter localization method comprising: performing an image sketchingstep to sketch a face image from the image; performing a frontal facegenerating step to transform the face image into a frontal face imageaccording to the frontal face generating model; performing an eye centermarking step to mark a frontal eye center position information on thefrontal face image according to the gradient method; and performing ageometric transforming step to calculate two rotating variables betweenthe face image and the frontal face image, and calculate the eye centerposition information according to the two rotating variables and thefrontal eye center position information; wherein the eye center markingstep comprises: performing a weight adjusting step to adjust a weightvalue of the frontal face image according to an Iris-Ripple filtermethod.
 6. The eye center localization system of claim 5, wherein theimage sketching step comprises: performing a facial feature marking stepto mark a chin feature point, a right eye feature point and a left eyefeature point on the image; and performing a facial area sketching stepto sketch the face image according to the chin feature point, the righteye feature point and the left eye feature point.
 7. The eye centerlocalization system of claim 5, wherein the geometric transforming stepcomprises: performing a rotating variable calculating step to calculatethe two rotating variables between the face image and the frontal faceimage according to a linear relation equation; and performing an eyecenter transforming step to predict a depth transforming coordinate ofthe face image with respect to the frontal face image, and calculate theeye center position information according to the depth transformingcoordinate.
 8. The eye center localization system of claim 5, whereinthe processing unit further comprises: performing a model training stepto train the face image, the eye center position information, thefrontal face image and the frontal eye center position information togenerate an eye center locating model.